Enhanced blocking block area method for segmentation of continuous speech

Abstract

Abstract Segmentation is one of the important stages in the speech recognition in the type of continuous speech. The segmentation phase serves to break the sentences into words that can be recognized by the computer. The quality of segmentation results can affect the results of the recognition that is done. This research examines the dynamic threshold used in the process of continuous speech segmentation and also proposes an Enhanced Blocking Block Area method in the Indonesian language domain. Three algorithms were compared (K-Means, Fuzzy C-Means, and Otsu) to find the best dynamic threshold and add morphological operations and overlapping to the blocking block area method to obtain the best segmentation accuracy. Based on the results of the research, the Fuzzy C-Means algorithm provides the best threshold results compared to the other two algorithms. By using the Fuzzy C-Means algorithm with the addition of morphology and overlapping, this study can improve the accuracy of continuous speech segmentation in the Indonesian Language from 24% to 90%.